Liver cancer prediction system for early detection and control method thereof

ABSTRACT

The present invention relates to a liver cancer prediction system for early detection and control method thereof, which can perform hierarchical classification relating to a risk group for hepatocellular carcinoma, through an estimation of the incidence rate for the hepatocellular carcinoma and a relative risk of the incidence of hepatocellular carcinoma, both of which are found on an individual basis. General information on a patient, information depending on an ultrasonic test performed, clinical information including information on findings upon a first registration of a patient and information on findings upon a diagnosis of liver cancer, and information on a risk group, are stored in a database. A regression count which is an attributable ratio corresponding to each of risk factors is calculated based on the clinical information and risk group information stored in the database. An odds ratio of the incidence of liver cancer is measured by calculating risk probability of the incidence of liver cancer through a given operation process using the calculated regression count. It is thus possible to prevent the incidence of liver cancer per person depending on prediction of the incidence of liver cancer. Also hierarchical classification relating to a risk group for hepatocellular carcinomas is performed through the incidence rate for hepatocellular carcinoma and a relative risk of the incidence of liver cancer that are calculated on an individual basis. Therefore, a tailored model for prediction the incidence of liver cancer can be constructed.

TECHNICAL FIELD

The present invention relates to a liver cancer prediction system forearly detection and control method thereof. More particularly, thepresent invention relates to a liver cancer prediction system for earlydetection and control method thereof, which can predict liver cancerusing an individual tailor-made model for predicting the incidence ofliver cancer, in such a manner that hierarchical classification relatingto a risk group for hepatocellular carcinoma is performed inconsideration of an estimation of the incidence rate of hepatocellularcarcinoma and a relative risk of the incidence of the hepatocellularcarcinoma on a basis of information on risk factors, through thecollection of prospective materials for analyzing a result of along-term ultrasonic inspection.

BACKGROUND ART

Liver cancer refers to a malignant tumor generated within the liver. Itcan be largely classified into hepatocellular carcinoma that isprimarily generated within a hepatocyte, and metastatic liver cancerthat is generated within extrahepatics and is then transferred to theinside of the liver. Liver cancer in this case means primaryhepatocellular carcinoma. The hepatocellular carcinoma is one of themost common malignant tumors worldwide. The incidence rate of thehepatocellular carcinoma differs greatly from region to region. It isreported that hepatocellular carcinoma-prone regions are Africa and EastAsia, where the incidence rate of hepatocellular carcinoma is 20 andabove per 100,000 people. Whereas it is reported that the incidence rateof hepatocellular carcinoma is 10 or less per 100,000 people in U.S.A.,North Europe, etc., with a relatively low incidence rate of thisdisease. Korea has a high incidence rate of hepatocellular carcinomasuch as 30 per 100,000 male population and 7 per 100,000 femalepopulations. Especially the incidence rate aged 40 to 60 is 74 in maleand 15 in female, which is very high worldwide. Korea NationalStatistical Office reports that Korea has the second highest death ratein liver cancer next to Africa. According to the report on cancer deathrate published by Korea National Statistical Office in 1996, about10,000 persons died of this disease in a year, which shows a livercancer death rate of 21.4%. This ratio is the second highest cancerafter gastric cancer. The Office reports that the death rate of livercancer in the forties to fifties is even higher than that of gastriccancer.

In order to prevent liver cancer, it is required that we must exactlyknow the incidence carcinogenesis of liver cancer. If there aremedicines for completely hindering carcinogenesis, it will be possibleto easily prevent cancer. In recent years, an effort to prevent cancerwith medicines has been actively made. In terms of liver cancer,however, significant advancements have not yet been reported so far.Although there has been proposed a method of administrating a medicinethat changes aflatoxin within the body to non-carcinogens in someregions where people are severely exposed to aflatoxin, it is still onlyin a research stage. Therefore, even if it is uncertain to knowcarcinogenesis, the best alternative prevention method available has tobe driven. The most efficient method to prevent a liver caner is toremove or avoid risk factors for hepatocellular carcinoma.

The most widely used inspection methods for early detection ofhepatocellular carcinoma are a liver ultrasonic inspection method and aserum alpha-feto protein level checking method. Computerized tomography(CT) is more accurate to detect the incidence of cancer than theultrasonic inspection method, but it is impractical for a screening testdue to the inconvenience and high cost. Meanwhile, the ultrasonicinspection method is easy to use and has a detection sensitiveness ofabout 75% or more for even a tumor of a size less than 3 cm.Accordingly, the ultrasonic inspection method has been widely used forthe screening test for early detection of liver cancer.

However, there is no method to analyze the long term individualultrasonic inspection for prediction of the incidence of liver cancer. Apatient has to suffer from an inconvenience of receiving diagnosis ofliver cancer through the ultrasonic inspection method every time.

DISCLOSURE OF INVENTION

Accordingly, the present invention has been made in view of the aboveproblems. The present invention provides a liver cancer predictionsystem for early detection and control method thereof, which can performhierarchical classification relating to a risk group for hepatocellularcarcinoma, through an estimation of the incidence rate for thehepatocellular carcinoma and a relative risk of the incidence ofhepatocellular carcinoma, both of which are found on an individualbasis.

To achieve the above objects, according to one aspect of the presentinvention, there is provided a liver cancer prediction system for earlydetection, including a controller for controlling the entire operationof the system; a display unit for displaying information and a graphicuser interface depending on the operation of the system under thecontrol of the controller; an input unit for inputting initial setvalues, selecting a given menu based on the information displayed on thedisplay unit and inputting information corresponding to the selectedmenu; a plurality of databases for storing general information on apatient, information depending on an ultrasonic test performed, clinicalinformation including information on findings upon a first registrationof a patient and information on findings upon a diagnosis of livercancer, and information on a risk group; a regression counter forcalculating a regression count which is an attributable ratiocorresponding to each of risk factors based on the clinical informationand risk group information stored in the database; and an odds ratiomeasurement unit for measuring an odds ratio of the incidence of livercancer by calculating risk probability of the incidence of liver cancerthrough a given operation process using the regression count calculatedin the regression counter.

According to another aspect of the present invention, there is alsoprovided a method of controlling liver cancer prediction systemincluding a controller for controlling the entire operation of thesystem; a display unit for displaying information depending on theoperation of the system and a graphic user interface under the controlof the controller; an input unit for inputting initial set values,selecting a given menu according to information displayed on the displayunit and inputting information corresponding to the selected menu; aplurality of databases for storing general information on a patient,information depending on an ultrasonic operation performed, clinicalinformation including information on findings upon a first registrationof a patient and information on findings upon a diagnosis of livercancer, and information on a risk group; a regression counter forcalculating a regression count which is an attributable ratiocorresponding to each of risk factors based on clinical information andrisk group information stored in the database; and an odds ratiomeasurement unit for measuring an odds ratio of the incidence of livercancer by calculating risk probability of the incidence of liver cancerthrough a given operation process using the regression count calculatedin the regression counter, comprising: a patient information-managingstep of displaying, on a display unit, a given menu wherein generalinformation on a patient can be written, and storing informationinputted through the input unit in the database; an ultrasonicinformation-managing step of displaying, on the display unit, acorresponding menu wherein information depending on an ultrasonic testperformed can be written, and storing information inputted through theinput unit in the database; a clinical information-managing step ofdisplaying, on the display unit, a given menu wherein information onfindings upon a first registration of a patient and information onfinding upon a diagnosis of liver cancer can be written, and storinginformation inputted through the input unit in the database; a riskgroup-assigning step of displaying, on the display unit, a menu of agiven format wherein additional risk groups can be assigned after theclinical information-managing step, and storing s risk group assignedaccording to information inputted through the input unit in thedatabase; and an odds ratio measurement step of measuring an odds ratioof the incidence of liver cancer, by calculating probability of theincidence of liver cancer on the basis of clinical information stored inthe clinical information-managing step and the risk group assigned inthe risk group-assigning step.

BRIEF DESCRIPTION OF DRAWINGS

Further objects and advantages of the invention can be more fullyunderstood from the following detailed description taken in conjunctionwith the accompanying drawings in which:

FIG. 1 is a diagram showing the connection of a prediction system basedon the embodiment of the present invention,

FIG. 2 is a block diagram illustrating the construction of a predictionsystem based on the embodiment of the present invention;

FIG. 3 is a block diagram illustrating the construction of a web serverbased on the embodiment of the present invention;

FIG. 4 is a block diagram illustrating the construction of a predictionserver based on the embodiment of the present invention;

FIG. 5 is a block diagram illustrating the entire configuration of agraphic user interface (GUI) of the prediction system based on theembodiment of the present invention;

FIG. 6 is an exemplary view showing a GUI of an initial main menu basedon the present invention;

FIG. 7 is an exemplary view showing a GUI of a patientinformation-managing menu based on the present invention;

FIG. 8 is an exemplary view showing a GUI of an ultrasonicinformation-managing menu based on the present invention;

FIG. 9 is a table showing a list of information stored in a databasebased on the present invention;

FIG. 10 is an exemplary view showing a GUI of a clinicalinformation-managing menu based on the present invention;

FIG. 11 is an exemplary view showing a GUI of a risk group-assigningmenu based on the present invention;

FIG. 12 is an exemplary view showing a GUI of a core risk factor menubased on the present invention;

FIG. 13 is an exemplary view showing a GUI of an extended risk factormenu based on the present invention;

FIG. 14 is an exemplary view showing a list of risk factors formeasuring an odds ratio based on the present invention;

FIG. 15 is the entire flowchart that explains a process of predictingliver cancer based on the present invention; and

FIG. 16 is a flowchart that explains a process of a result notificationbased on the present invention.

BEST MODE FOR CARRYING OUT THE INVENTION

The present invention will now be described in detail with reference tothe accompanying drawings.

FIG. 1 is a diagram showing the connection of the prediction systembased on the embodiment of the present invention.

Referring to FIG. 1, the prediction system 400 of the present inventionis connected to a user computer terminal 20 and a plurality of hospitalservers 300 through the Internet 200. The system 400 can transmit shortmessages to a user mobile communication terminal 10 via a mobilecommunication network 100.

The mobile communication network includes a base transceiver system(hereinafter, referred to as “BTS”) 110 that communicates by wirelesswith a user's mobile communication terminal 10, a base stationcontroller (hereinafter, referred to as “BSC”) 120 that controls the BTS110, a mobile switching center (hereinafter, referred to as “MSC”) 130connected to the BSC 120 for performing call switching, and a shortmessage servicing center (hereinafter, referred to as “SMSC”) 140connected to the MSC 130 to control the short message.

Also a packet data-servicing node (hereinafter, referred to as “PDSN”)150 for servicing packet data is connected to the BSC 120 of the mobilecommunication network 100. The PDSN 150 can provide an Internet 200connection to the mobile communication terminal 10 via a data corenetwork (hereinafter, referred to as “DCN”) 160.

FIG. 2 is a block diagram illustrating the construction of a predictionsystem based on the embodiment of the present invention.

Referring to FIG. 2, the prediction system 400 includes a web server 410that provides a web service to the user computer terminal 20 through theInternet 200, a prediction server 420 which detects a liver cancer, anda database 430 which stores/manages data.

The web server 410 provides a web service for predicting liver cancer tothe connected user computer terminal 20 through the Internet 200. Theweb server 410 provides data relating to liver cancer prediction in theform of a short message or E-mail to the user terminal 10 or 20, incooperation with the prediction server 420.

The prediction server 420 controls the plurality of the hospital servers300 to collect/manage patient information through the Internet 200.

Further, the database 430 includes a patient information database 431for storing/managing information on a patient, an ultrasonic informationdatabase 432 for storing/managing ultrasonic information, a clinicalinformation database 433 for storing/managing clinical information on apatient, and a risk group information database 434 for storing/managinginformation on a risk group.

FIG. 3 is a block diagram illustrating the construction of the webserver 410 based on the embodiment of the present invention.

Referring to FIG. 3, the web server 410 includes a controller 411 forcontrolling the entire operation, a network connection unit 412 forconnecting to the Internet 200, a web service unit 413 for providing theuser terminal 10 or 20 with a web service through the Internet 200, anshort message servicing (hereinafter, referred to as “SMS”) managementunit 417 for generating a short message and providing it to a user'smobile communication terminal 10 through the mobile communicationnetwork 100, an E-mail management unit 418 for generating E-mail andtransmitting it to an E-mail account of a user, and a prediction servercooperation unit 419 that cooperates with the prediction server 420.

The web server 410 which was constructed by the present inventionprovides a web service for predicting liver cancer to the user terminal10 or 20 that is connected to the Internet 200 through the web serviceunit 413. In this case, a user's mobile communication terminal 10 isconnected to the Internet 200 in a wireless manner via the mobilecommunication network. A user computer terminal 20 is connected to theInternet through a wired network.

The web server 410 receives the result about the liver cancer predictionfrom the prediction server 420 via the prediction server cooperationunit 419, and generates a short message containing the result throughthe SMS management unit 417. Furthermore, the web server 410 transmitsthe generated short message to a mobile communication terminal 10 of theattending physician of a corresponding patient who is previouslyregistered through the mobile communication network 100.

In addition, the web server 410 generates E-mail containing the resultthrough the E-mail management unit 418, and transmits the generatedE-mail to an E-mail account of the attending physician of thecorresponding patient.

FIG. 4 is a block diagram illustrating the construction of theprediction server 420 for early detection of liver cancer based on theembodiment of the present invention.

Referring to FIG. 4, the prediction server 420 includes a controller 422for controlling the entire operation, a display unit 428 for performinga window display so that information depending on the operation of thecontroller 422 can be visually viewed, an input unit 421 for inputtinggiven data or commands based on the information displayed on the displayunit 428, a network connection unit 423 a for connecting the predictionserver 420 and the Internet, a web server cooperation unit 423 b forexchanging data with the web server 410, and a database cooperation unit423 c for accessing the database 430 to store/manage data.

The prediction server 420 further has a regression counter 425 forcalculating a regression count which is an attributable ratiocorresponding to each risk factor based on the clinical information andthe risk group information stored in the database 430, and an odds ratiomeasurement unit 424 a for measuring an odds ratio of the occurrence ofa liver cancer by calculating incidence probability of liver cancerthrough a given operation process using the regression count calculatedin the regression counter 425. In the above, the odds ratio measurementunit 424 a is connected to an odds ratio storage unit 424 b for storingthe odds ratio and risk probability that are previously produced.

The prediction server 420 further has a calculation-selecting unit 426for selecting whether to calculate risk probability using core riskfactors or using an extended model. The prediction server 420 alsoincludes a trace search unit 427 for searching a trace monitoring itemfrom risk group-assigning materials that are previously stored in thedatabase when the extended risk factor is selected.

The prediction server 420 transmits the result on the calculated riskprobability to the web server 410 so that it can be transmitted to theattending physician of a corresponding patient in the form of a shortmessage or E-mail.

An operational process based on the present invention will now bedescribed with reference to the drawings.

FIG. 5 is a block diagram illustrating the entire configuration of agraphic user interface (GUI) of the prediction system based on theembodiment of the present invention. FIG. 6 and FIG. 14 are exemplaryviews showing applications of the graphic user interface shown in FIG. 5and the configuration of a data table.

Referring to FIG. 5, when the prediction system of the present inventionis executed, an initial window M10 is displayed and then a main windowM20 as shown in FIG. 6 is displayed on the display unit 428 of theprediction server 420. The GUI of the main window has a file window M31that supports storage/conversion/deletion of data, database conversion,etc., a diagnosis contents input menu M32 for executing patient datainput, ultrasonic data input and clinical data input, and a riskgroup-assigning menu M33 for assigning a risk group. Based upon theselection by the input unit 421, the controller 422 of the predictionserver 420 displays a GUI for inputting corresponding data on thedisplay unit 428 and receives the input data through the input unit 421.

Meanwhile, if a command to select the diagnosis contents input menu M32is inputted through the input unit 421, the controller 422 controls thedisplay unit 428 to display GUIs of a patient information-managing menuM41, an ultrasonic information-managing menu M42 and a clinicalinformation-managing menu M43. The details are as follows. If a commandto select the patient information-managing menu M41 is inputted throughthe input unit 421, the controller 422 controls the display unit 428 todisplay a GUI as shown in FIG. 7. Accordingly, a user can create, store,modify, delete, cancel, inquire registration information and personalinformation on a patient, or finish the menu.

If registration information or personal information on a patient isinputted through the graphic user interface and the input unit 421, thecontroller 422 of the prediction server 420 controls the databasecooperation unit 423 c to create, store, modify, delete, cancel, orinquire the data in the patient information database 431 of the database430.

Meanwhile, if a command to select the ultrasonic information-managingmenu M42 is inputted through the input unit 421, the controller 422controls the display unit 428 to display a GUI as shown in FIG. 8. Theultrasonic information-managing menu M42 serves to input informationdepending on an ultrasonic test. If a menu execution command isinputted, the controller 422 first controls the display unit 428 todisplay the GUI. The controller 422 then controls the databasecooperation unit 423 c to request/receive registration information on acorresponding patient that is stored in the patient information database431, and then makes the received data to be displayed at a correspondingitem of the GUI.

The controller 422 controls the database cooperation unit 423 c tostore/manage ultrasonic information inputted through the input unit 421in the ultrasonic information database 432. At this time, the ultrasonicinformation is controlled to be matched to patient information and thenstored in the database 432. An embodiment of each of parameters ofpatient information and ultrasonic information is shown in FIG. 9.

Moreover, if a command to select the clinical information-managing menuM43 is inputted through the input unit 421, the controller 422 controlsthe display unit 428 to display a GUI as shown in FIG. 10.

The clinical information consists of parameters such as a diagnosissubject, hepatitis, diagnosis basis, a case history, examinationopinions, an odds ratio, and the like. If given data are inputtedthrough the input unit 421, the controller 422 controls the databasecooperation unit 423 c to store/manage the inputted data in acorresponding database.

In this case, if data for patient information are inputted through theinput unit 421, the controller 422 controls the database cooperationunit 423 c to search corresponding patient information through thepatient information database 431. If desired patient information isfound, the controller 422 controls the searched patient information tobe displayed at a corresponding item through the graphic user interface.Furthermore, the controller 422 searches clinical information coincidentwith corresponding patient information through the clinical informationdatabase 433. The controller 422 then controls the searched clinicalinformation to be displayed at a corresponding item of the graphic userinterface.

Meanwhile, if desired patient information is not found, the controller422 determines that the data are new patient information. Accordingly,the controller 422 controls the display unit 428 to display a messageindicating that “there is no matching information as a result of thesearch” and a message that prompts a user to input information throughthe input unit 421, for example, “There exists no such patientinformation. Please input the new patient information”. Also thecontroller 422 controls the database cooperation unit 423 c to storeinformation inputted via the input unit 421 in the database 430.

Meanwhile, if a command for the risk group-assigning menu M33 isinputted through the input unit 421, the controller 422 controls thedisplay unit 428 to display a GUI as shown in FIG. 11.

Then, the controller 422 searches patient information from the patientinformation database 431 where the data are inputted through the inputunit 421. The controller 422 controls the searched patient informationto be displayed through the GUI.

Furthermore, the controller 422 searches information on a risk groupcoincident with corresponding patient information from the risk groupdatabase 434. The controller 422 then controls information on thesearched risk group to be displayed at a corresponding item of the GUI.

In addition, when the controller 422 calculate the risk probability ofthe risk group the controller 422 can select whether the odds ratio willbe calculated using a core risk factor or an extended risk factor inaccordance with a command inputted through the input unit 421. At thistime, if a command to select the core risk factor is inputted via theinput unit 421, the controller 422 displays a graphic user interface asshown in FIG. 12. If a command to select the extended risk factor isinputted, the controller 422 controls the display unit 428 to display aGUI as shown in FIG. 13.

In the event that the command for the extended risk factor is inputted,the controller 422 determines whether a “history” command is inputted.If the history command is inputted through the input unit 421, thecontroller 422 controls the database cooperation unit 423 c by the tracesearch unit 427 and searches a trace monitoring item of a correspondingpatient from information on a risk group that is stored/managed in therisk group information database 434. Further, the controller 422controls the display unit 428 to display the searched information.

The risk factors based on the risk group information may consist ofhepatitis, liver cirrhosis, hepatitis furuncle, ALT, α-FP (fetoprotein), age, sex (man/female), tolerance level to alcohol, whetherdrinking history is known or not, probability for liver cancer, oddsratio, risk probability and a risk group, as shown in FIG. 14.

Meanwhile, if patient information, ultrasonic information, clinicalinformation and information on the risk group are each inputtedaccording to the above procedure, the controller 422 calculates the oddsratio based on the inputted information. There are two methods tocalculate the odds ratio. The controller 422 may use a method ofcalculating an odds ratio using three kinds of core risk factors such asthe diagnosis subject, the cause of hepatitis and alpha fetoprotein(hereinafter, referred to as “AFP”), or a method of calculating an oddsratio using an extended risk factors where other control factorsincluding the three kinds of the core risk factors are taken intoconsideration. The controller 422 selects a calculation method throughthe calculation-selecting unit 426 based on the selection of the corerisk factors or the extended risk factors in the above-mentionedprocedure.

When a method of calculating the odds ratio is decided, the controller422 calculates a regression count (attributable ratio) corresponding toeach risk factor shown in FIG. 14 through the regression counter 425. Inother words, the controller 422 defines three kinds of risk factors(diagnosis subject, the cause of hepatitis, and AFP) for setting alogistic regression model as numerical parameters and then calculatesthe attributable ratio of each of the three kinds of the risk factors inthe regression counter 425.

Thereafter, the odds ratio measurement unit 424 a calculates riskprobability through the logistic regression model using the calculatedregression count under the control of the controller 422. That is, theodds ratio measurement unit 424 a calculates a new odds ratio and a riskprobability value by inserting the regression count for each of the riskfactors calculated in the regression counter 425 and a statisticalprediction model depending on the risk probability and odds ratio of theincidence of liver cancer that are previously produced into the logisticregression calculation formula. The controller 422 controls the displayunit 428 to display the new calculated risk probability value.

The odds ratio measurement unit 424 a updates/stores information on thecalculated risk probability in the odds ratio storage unit 424 b. Atthis time, the controller 422 transmits the result to the web server 410via the web server cooperation unit 423 b. The web server 410 cangenerate a short message containing the result through the SMSmanagement unit 417, and then transmit the short message to the mobilecommunication terminal 10 of the attending physician that is previouslyregistered, through the mobile communication network 100. In addition,the web server 410 can generate E-mail containing the result through theE-mail management unit 418 and then transmit the generated E-mail to anE-mail account of the attending physician that is previously registered,through the Internet 200.

The control method as described above will now be described withreference to a flowchart.

FIG. 15 is the entire flowchart for explaining a process of predictionliver cancer based on the present invention.

Referring to FIG. 15, when a system is driven (S100), the controller 422of the prediction server 420 controls the display unit 428 to displayinitial and main windows (S110). The controller 422 then determineswhether a command inputted through the input unit 421 is a command toinput diagnosis contents (S120) or not.

If the command is a command to input diagnosis contents, the controller422 determines whether a command for the patient information-managingmenu can be inputted through the display unit 422 (S130). When thecommand for the patient information-managing menu can be inputted, thecontroller 422 outputs a message that prompts a user to input data onthe display unit 428. The controller 422 then stores the data (S131)inputted through the input unit 421 in the patient information database431 (S132).

If the command is not for the patient information-managing menu in stepS130, the controller 422 determines whether the command is for theultrasonic information-managing menu (S140). If the command is for theultrasonic information-managing menu in step S140, the controller 422outputs a message that prompts a user to input data on the display unit428. The controller 422 then stores the data (S141) inputted through theinput unit 421 in the ultrasonic information database 432 (S142).

If the command is not for the ultrasonic information-managing menu instep S140, the controller 422 determines whether the command is for aclinical information-managing menu (S150). If the command is for theclinical information-managing menu in step S150, the controller 422outputs a message that prompts a user to input data on the display unit428. The controller 422 then stores the data (S151) inputted through theinput unit 421 in the clinical information database 433 (S152).

Meanwhile, if it is determined that the command is not the commandinstructing to input diagnosis contents in step S120, the controller 422determines whether a command for the risk group-assigning menu isinputted (S160). If the command for the risk group-assigning menu isinputted, the controller 422 assigns a core risk factor or an extendedrisk factor based on information inputted through the input unit 421(S161). The regression counter 425 then calculates a regression countthat is assigned under the control of the controller 422 (S162).

When the regression count is calculated in step S162, the odds ratiomeasurement unit 424 a uses the regression count to calculate an oddsratio under the control of the controller 422 (S163). The controller 422controls the display unit 428 to display the calculated odds ratio. Atthe same time, the controller 422 controls the calculated odds ratio tobe stored in the odds ratio storage unit 424 b (S164).

Furthermore, the controller 422 transmits the result in step S163 to theweb server 410 through the web server cooperation unit 423 b. The webserver 410 generates a short message containing the result through theSMS management unit 417 and then transmits the short message to themobile communication terminal 10 of the attending physician of acorresponding patient, which is registered in advance, through themobile communication network 100 (S165). In this case, the web server410 may generate E-mail containing the result through the E-mailmanagement unit 418 and transmit the E-mail to an E-mail account of theattending physician of a corresponding patient that is registered inadvance.

A process of notifying the result through the short message or theE-mail in step S165 will now be described.

FIG. 16 is a flowchart for explaining a process of notifying the resultbased on the present invention.

Referring to FIG. 16, the prediction server 420 transmits the result tothe web server 410 through the web server cooperation unit 423 b (S165).Accordingly, the controller 411 of the web server 410 transfers thereceived result to the SMS management unit 417 (S210). The SMSmanagement unit 417 that received the result generates a short message(SMS) containing the result (S220) and then transmits the short messageto the mobile communication network 100 (S230). The mobile communicationnetwork 100 transfers the short message to the mobile communicationterminal 10 of the attending physician (S240).

Meanwhile, the controller 411 of the web server 410 transfers thereceived result to the E-mail management unit 418 (S250). The E-mailmanagement unit 418 that received the result generates E-mail containingthe result (S260) and transmits the generated E-mail to an E-mailaccount of the attending physician through the Internet 200 (S270).

Accordingly, the attending physician of a patient can receive the resultof liver cancer prediction in the form of the short message or E-mailthrough the mobile communication terminal. Therefore, the physician canconsistently monitor the odds ratio of a patient and can take immediateaction in case of emergency.

INDUSTRIAL APPLICABILITY

As described above, based on the present invention, an estimation of theincidence rate for hepatocellular carcinoma and a relative risk of theincidence of liver cancer is calculated on an individual basis. It isthus possible to prevent the incidence of liver cancer by individualdepending on the prediction of the incidence of liver cancer.

Furthermore, according to the present invention, hierarchicalclassification relating to a risk group for hepatocellular carcinoma isperformed through an estimation of the incidence rate for hepatocellularcarcinoma and a relative risk of the incidence of liver cancer, both ofwhich are found on an individual basis. The present invention has aneffect in that a tailored model for predicting the incidence of livercancer is constructed.

In addition, based on the present invention, the attending physician ofa patient can receive a result of liver cancer prediction in the form ofa short message or E-mail through his or her mobile communicationterminal. Therefore, the present invention has an effect in that thephysician can consistently monitor the odds ratio of a patient and takeimmediate action in case of emergency.

(While the present invention has been described with reference to theparticular illustrative embodiments, it is not to be restricted by theembodiments but only by the appended claims. It is to be appreciatedthat those skilled in the art can change or modify the embodimentswithout departing from the scope and spirit of the present invention.

1. A liver cancer prediction system for early detection, comprising: acontroller for controlling the entire operation of the system; a displayunit for displaying information and a graphic user interface dependingon the operation of the system under the control of the controller; aninput unit for inputting initial set values, selecting a given menubased on the information displayed on the display unit and inputtinginformation corresponding to the selected menu; a plurality of databasesfor storing general information on a patient, information depending onan ultrasonic test performed, clinical information including informationon findings upon a first registration of a patient and information onfindings upon a diagnosis of liver cancer, and information on a riskgroup; a regression counter for calculating a regression count which isan attributable ratio corresponding to each of risk factors based on theclinical information and risk group information stored in the database;and an odds ratio measurement unit for measuring an odds ratio of theincidence of liver cancer by calculating risk probability of theincidence of liver cancer through a given operation process using theregression count calculated in the regression counter.
 2. The livercancer prediction system as claimed in claim 1, wherein the databasecomprises a patient information database for storing/managinginformation on a patient, an ultrasonic information database forstoring/managing ultrasonic information, a clinical information databasefor storing/managing clinical information on the patient, and a riskgroup information database for storing/managing information on the riskgroup.
 3. The liver cancer prediction system as claimed in claim 1,wherein the controller receives, from the database, registrationinformation on a corresponding patient that is stored by default uponentry of information depending on the ultrasonic test, and then displayssuch information on an activation window automatically.
 4. The livercancer prediction system as claimed in claim 1, wherein clinicalinformation includes parameters such as a diagnosis subject, hepatitis,a diagnosis basis, a case history, examination findings and an oddsratio.
 5. The liver cancer prediction system as claimed in claim 1,wherein the odds ratio measurement unit uses three kinds of core riskfactors including a diagnosis subject, the cause of hepatitis and AFP inorder to calculate the risk probability.
 6. The liver cancer predictionsystem as claimed in claim 1, wherein the odds ratio measurement unituses extended risk factors where other control factors including thethree kinds of the core risk factors are taken into consideration inorder to calculate the risk probability.
 7. The liver cancer predictionsystem as claimed in claim 1, wherein the odds ratio measurement unituses risk factors consisting of hepatitis, liver cirrhosis, hepatitisfuruncle, ALT, α-FP (feto protein), age, sex (man/female), tolerancelevel to alcohol, where drinking history is not known, probability forliver cancer, an odds ratio, risk probability and a risk group.
 8. Theliver cancer prediction system as claimed in claim 1, wherein the oddsratio measurement unit finds an attributable ratio (regression count) byusing the logistic regression that corresponds to the risk factor andthen calculates risk probability.
 9. The liver cancer prediction systemas claimed in claim 1, wherein the odds ratio measurement unit comprisesan odds ratio storage unit for storing risk probability and an oddsratio of the incidence of liver cancer that are previously made.
 10. Theliver cancer prediction system as claimed in claim 1, wherein thecontroller comprises a calculation-selecting unit for selecting whetherto calculate risk probability using a core risk factor or performcalculation using an extended risk factor.
 11. The liver cancerprediction system as claimed in claim 1, wherein the controllercomprises a trace search unit for searching a trace monitoring item fromrisk group-assigning materials that are previously stored in thedatabase, in case where an extended risk factor is selected.
 12. Theliver cancer prediction system as claimed in claim 1, further comprisingan SMS management unit for generating a short message containinginformation on the result measured in the odds ratio measurement unit,and then providing the short message to a mobile communication terminalof a patient's attending physician that is previously registered througha mobile communication network.
 13. The liver cancer prediction systemas claimed in claim 1, further comprising an E-mail management unit forgenerating E-mail containing information on the result measured in theodds ratio measurement unit and transmitting the generated E-mail to anE-mail account of a patient's attending physician that is previouslyregistered.
 14. A liver cancer prediction system for early detection,comprising: a web server for providing a web service for predictingliver cancer to a user terminal through the Internet; a database forstoring general information on a patient, information depending on anultrasonic operation performed, clinical information includinginformation on findings upon a first registration of a patient andinformation on findings upon a diagnosis of liver cancer, andinformation on a risk group; and a predicting server for performingliver cancer prediction based on information of the database.
 15. Theliver cancer prediction system as claimed in claim 14, wherein the webserver comprises a controller for controlling the entire operation, anetwork connection unit for connection to the Internet, a web serviceunit for providing a web service for predicting liver cancer to the userterminal connected through the Internet, and a prediction servercooperation unit that cooperates with the prediction server to exchangedata with the prediction server.
 16. The liver cancer prediction systemas claimed in claim 15, wherein the web server receives information onan odds ratio of the incidence of liver cancer calculated in theprediction server through the prediction server cooperation unit, andthe web server further comprises an SMS management unit for generating ashort message containing received information on the odds ratio of theincidence of liver cancer and then providing the short message to amobile communication terminal of a patient's attending physician that ispreviously registered through a mobile communication network.
 17. Theliver cancer prediction system as claimed in claim 15, wherein the webserver receives information on an odds ratio of the incidence of livercancer calculated in the prediction server through the prediction servercooperation unit, and the web server further comprises an E-mailmanagement unit for generating E-mail containing received information onthe odds ratio of the incidence of liver cancer and providing thegenerated E-mail to an E-mail account of a patient's attending physicianthat is previously registered.
 18. The liver cancer prediction system asclaimed in claim 14, wherein the prediction server comprises: acontroller for controlling the entire operation of the system, a displayunit for displaying information depending on the operation of the systemand a graphic user interface under the control of the controller, aninput unit for inputting initial set values, selecting a given menuaccording to information displayed on the display unit and inputtinginformation corresponding to the selected menu, a regression counter forcalculating a regression count which is an attributable ratiocorresponding to each of risk factors based on clinical information andrisk group information stored in the database, and an odds ratiomeasurement unit for measuring an odds ratio of the incidence of livercancer by calculating risk probability of the incidence of liver cancerthrough a given operation process using the regression count calculatedin the regression counter.
 19. The liver cancer prediction system asclaimed in claim 18, wherein the controller receives, from the database,registration information on a corresponding patient that is stored bydefault when information depending on the ultrasonic test is inputted,and then displays such information on an activation windowautomatically.
 20. The liver cancer prediction system as claimed inclaim 18, wherein clinical information includes parameters such as adiagnosis subject, hepatitis, a diagnosis basis, a case history,examination findings and an odds ratio.
 21. The liver cancer predictionsystem as claimed in claim 18, wherein the odds ratio measurement unituses three kinds of core risk factors including a diagnosis subject, thecause of hepatitis and AFP in order to calculate the risk probability.22. The liver cancer prediction system as claimed in claim 18, whereinthe odds ratio measurement unit uses extended risk factors where othercontrol factors including the three kinds of the core risk factors aretaken into consideration in order to calculate the risk probability. 23.The liver cancer prediction system as claimed in claim 18, wherein theodds ratio measurement unit uses extended risk factors consisting ofhepatitis, liver cirrhosis, hepatitis furuncle, ALT, α-FP (fetoprotein), age, sex (man/female), tolerance level to alcohol, wheredrinking history is not known, probability for liver cancer, an oddsratio, risk probability and a risk group.
 24. The liver cancerprediction system as claimed in claim 18, wherein the odds ratiomeasurement unit finds an attributable ratio (regression count)corresponding to the risk factor and then calculates risk probabilityusing the logistic regression.
 25. The liver cancer prediction system asclaimed in claim 18, wherein the odds ratio measurement unit comprisesan odds ratio storage unit for storing risk probability and an oddsratio of the incidence of liver cancer that are previously made.
 26. Theliver cancer prediction system as claimed in claim 18, wherein thecontroller comprises a calculation-selecting unit for selecting whetherto calculate risk probability using a core risk factor or using anextended risk factor.
 27. The liver cancer prediction system as claimedin claim 18, wherein the controller comprises a trace search unit forsearching a trace monitoring item from risk group-assigning materialsthat are previously stored in the database, in case where an extendedrisk factor is selected.
 28. The liver cancer prediction system asclaimed in claim 14, wherein the database comprises a patientinformation database for storing/managing information on a patient, anultrasonic information database for storing/managing ultrasonicinformation, a clinical information database for storing/managingclinical information on the patient, and a risk group informationdatabase for storing/managing information on the risk group.
 29. Amethod of controlling liver cancer prediction system including acontroller for controlling the entire operation of the system; a displayunit for displaying information depending on the operation of the systemand a graphic user interface under the control of the controller; aninput unit for inputting initial set values, selecting a given menuaccording to information displayed on the display unit and inputtinginformation corresponding to the selected menu; a plurality of databasesfor storing general information on a patient, information depending onan ultrasonic operation performed, clinical information includinginformation on findings upon a first registration of a patient andinformation on findings upon a diagnosis of liver cancer, andinformation on a risk group; a regression counter for calculating aregression count which is an attributable ratio corresponding to each ofrisk factors based on clinical information and risk group informationstored in the database; and an odds ratio measurement unit for measuringan odds ratio of the incidence of liver cancer by calculating riskprobability of the incidence of liver cancer through a given operationprocess using the regression count calculated in the regression counter,comprising: a patient information-managing step of displaying, on adisplay unit, a given menu wherein general information on a patient canbe written, and storing information inputted through the input unit inthe database; an ultrasonic information-managing step of displaying, onthe display unit, a corresponding menu wherein information depending onan ultrasonic test performed can be written, and storing informationinputted through the input unit in the database; a clinicalinformation-managing step of displaying, on the display unit, a givenmenu wherein information on findings upon a first registration of apatient and information on finding upon a diagnosis of liver cancer canbe written, and storing information inputted through the input unit inthe database; a risk group-assigning step of displaying, on the displayunit, a menu of a given format wherein additional risk groups can beassigned after the clinical information-managing step, and storing srisk group assigned according to information inputted through the inputunit in the database; and an odds ratio measurement step of measuring anodds ratio of the incidence of liver cancer, by calculating probabilityof the incidence of liver cancer on the basis of clinical informationstored in the clinical information-managing step and the risk groupassigned in the risk group-assigning step.
 30. The method as claimed inclaim 29, wherein the ultrasonic information-managing step includesreceiving, from the database, registration information on acorresponding patient that is stored by default when informationdepending on the ultrasonic test is inputted, and then displaying suchinformation on the display unit.
 31. The method as claimed in claim 29,wherein clinical information-managing step includes displaying clinicalinformation including a diagnosis subject, hepatitis, a diagnosis basis,a case history, examination findings and an odds ratio, matchinginformation selected or inputted through the input unit to respectivefactors of the clinical information and then storing the matched resultsin the database.
 32. The method as claimed in claim 29, wherein theclinical information of the clinical information-managing step comprisesdetail parameters such as a serial number, a diagnosis subject,hepatitis, a diagnosis basis, a case history, examination findings,findings upon a diagnosis of liver cancer, a diagnosis method.
 33. Themethod as claimed in claim 29, wherein the clinical information-managingstep comprises: first step of, after a serial number is inputted into aregistration number inspection box and an enter key is then pressed,searching the serial number through a patient information table storedin the database; second step of, as a result of the search in the firststep, if the serial number is not present in the table, displaying amessage indicating that the serial number does not exist on the displayunit and if the serial number is present in the table, displayinginformation on a name, an attending physician and reason on the displayunit, and determining whether corresponding clinical information isstored in the database; and third step of, as a result of thedetermination in the second step, if corresponding clinical informationdoes exist in the database, displaying contents stored in the databaseon the display unit, and if corresponding clinical information does notexist in the database, displaying a given guide message on the displayunit.
 34. The method as claimed in claim 29, wherein the odds ratiomeasurement step includes using three kinds of core risk factorsincluding a diagnosis subject, the cause of hepatitis and AFP in orderto calculate the risk probability.
 35. The method as claimed in claim29, wherein the odds ratio measurement step includes using an extendedrisk factor where other control factors including the three kinds of thecore risk factors are taken into consideration in order to calculate therisk probability.
 36. The method as claimed in claim 29, wherein theodds ratio measurement step includes using the risk factors consistingof hepatitis, liver cirrhosis, hepatitis furuncle, ALT, α-FP (fetoprotein), age, sex (man/female), tolerance level to alcohol, wheredrinking history is not known, probability for liver cancer, an oddsratio, risk probability and a risk group.
 37. The method as claimed inclaim 36, wherein the odds ratio measurement step includes finding anattributable ratio (regression count) corresponding to the risk factorand then calculates risk probability through logistic regression usingthe attributable ratio.
 38. The method as claimed in claim 37, whereinthe odds ratio measurement step comprises: a first step of definingthree core risk factors for setting a logistic regression model asnumerical type parameters; and a second step of inserting respectiverisk factor into a statistical prediction model depending in riskprobability and an odds ratio of the incidence of liver cancer that arealready made after being defined in the first step, thus displaying theodds ratio and a risk probability value based on the ratio on thedisplay unit.
 39. The method as claimed in claim 29, wherein the riskgroup-assigning step comprises: first step of searching a serial numberthrough a patient information table stored in the database after theserial number is inputted through the input unit and the enter key ispressed; second step of, as a result of the search in the first step, ifthe serial number is not present in the table, displaying a messageindicating that the serial number does not exist on the display unit,and if the serial number is present in the table, displaying informationon a name and an attending physician and reason on the display unit andat the same time determining whether ‘risk group specification’ isstored in the database; and third step of, as a result of thedetermination in the second step, if corresponding clinical informationis stored in the database, displaying contents stored in the database onthe display unit, and if corresponding clinical information is notstored in the database, displaying a given guide message on the displayunit.
 40. The method as claimed in claim 29, wherein the riskgroup-assigning step comprises a step of selecting whether to calculaterisk probability using a core risk factor or using an extended factor.41. The method as claimed in claim 40, wherein the risk group-assigningstep comprises a step of searching a trace monitoring item from riskgroup-assigning materials that are previously stored in the database, incase where an extended risk factor is selected.
 42. The method asclaimed in claim 29, wherein liver cancer prediction system furthercomprises an SMS management unit for managing a short message, and themethod further comprises: a step of generating a short messagecontaining the result calculated in the odds ratio measurement step; anda step of transmitting the short message to a mobile communicationterminal of a patient's attending physician that is previouslyregistered, through a mobile communication network.
 43. The method asclaimed in claim 29, wherein liver cancer prediction system furthercomprises an E-mail management unit for managing E-mail, and the methodfurther comprises: a step of generating E-mail containing the resultcalculated in the odds ratio measurement step; and a step oftransmitting the generated E-mail to an E-mail account of a patient'sattending physician that is previously registered.